• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3172-3193
• /
• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.5
• /
• pp.871-885
• /
• 2015

The improved DFSA for 2.4GHz multi-tags active RFID is suggested in 2 different ways: 1) simplified tag collection and Ack procedure using query command and 2) modified Schoute's method to control the number of slots in the frame. To evaluate the performance of the improved system we develop the simulation model. Varying the number of tags in the system we track the performance measures such as throughput, recognition time for multi-tags and tag recognition rate during a given time. The suggested method shows the best performance over all measures. Simplification of collection and Ack commands using query commands contributes to reducing tag recognition time. And the modified Schoute's method which controls the frame size using $k_1$ and $k_2$ contributes to throughput improvement and reduces target cognition time by reducing the number of collection rounds.

• The Journal of the Korea Contents Association
• /
• v.15 no.7
• /
• pp.30-42
• /
• 2015

In this paper, we propose a new skyline query processing scheme to enhance accuracy of query processing and communication cost in mobile P2P environments. The proposed scheme consists of three stages such as the pre-skyline processing, the query transmission range extension policy, and the continuous skyline query processing. In the pre-skyline processing, a peer selects the candidate filtering objects who have the potential to be selected. By doing so, the proposed scheme reduces the filtering cost when processing the query. In the query transmission range extension policy, we have improved the accuracy by extending the query transmission range. In addition, it can handle continuous skyline query by performing the monitoring after the first skyline query processing. In order to show the superiority of the proposed method, we compare it with the existing schemes through performance evaluation. As a result, it was shown that the proposed scheme outperforms the existing schemes.

• The KIPS Transactions:PartD
• /
• v.10D no.4
• /
• pp.661-670
• /
• 2003

Materialized views which are stored views of the database offer opportunities for significant performance gain in query valuation by providing fast access to pre-computed data. The question of when and how to use a materialized view in processing a given query is a difficult one attracting a significant amount of research. Whether a materialized view can be used in answering a query depends on the relationship between the view and the query. Proposed in this paper are new ways of utilizing materialized views in answering a query. Semantic relationships are used in addition to syntactic ones. We also utilize a materialized view in answering a query even if it has relations unrelated to the query. We first show the conditions for testing whether a materialized view can be utilized in answering a query and then present the algorithms for testing the conditions and reformulating a query with a materialized view.

• The Journal of the Korea Contents Association
• /
• v.14 no.10
• /
• pp.454-462
• /
• 2014

With the development of location aware technologies and mobile devices, location-based services have been studied. To provide location-based services, many researchers proposed methods for processing various query types with Mapreduce(MR). One of the proposed methods, is a Reverse k-nearest neighbor(RkNN) query processing method with MR. However, the existing methods spend too much cost to process the continuous RkNN query. In this paper, we propose an efficient continuous RkNN query processing method with MR to resolve the problems of the existing methods. The proposed method uses the 60-degree-pruning method. The proposed method does not need to reprocess the query for continuous query processing because the proposed method draws and monitors the monitoring area including the candidate objects of a RkNN query. In order to show the superiority of the proposed method, we compare it with the query processing performance of the existing method.

• The KIPS Transactions:PartD
• /
• v.16D no.4
• /
• pp.507-516
• /
• 2009

In stream data processing system, generally the interval queries are in advance registered in the system. When a data is input to the system continuously, for realtime processing, a query indexing method is used to quickly search queries. Thus, a main memory-based query index with a small storage cost and a fast search time is needed for searching queries. In this paper, we propose a LVC-based(Limited Virtual Construct-based) query index method using a hashing to meet the both needs. In LVC-based query index, we divide the range of a stream into limited virtual construct, or LVC. We map each interval query to its corresponding LVC and the query ID is stored on each LVC. We have compared with the CEI-based query indexing method through the simulation experiment. When the range of values of input stream is broad and there are many short interval queries, the LVC-based indexing method have shown the performance enhancement for the storage cost and search time.

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1437-1457
• /
• 2016

In outsourced databases, the cloud provides an authorized user with querying services on the outsourced database. However, sensitive data, such as financial or medical records, should be encrypted before being outsourced to the cloud. Meanwhile, k-Nearest Neighbor (kNN) query is the typical query type which is widely used in many fields and the result of the kNN query is closely related to the interest and preference of the user. Therefore, studies on secure kNN query processing algorithms that preserve both the data privacy and the query privacy have been proposed. However, existing algorithms either suffer from high computation cost or leak data access patterns because retrieved index nodes and query results are disclosed. To solve these problems, in this paper we propose a new kNN query processing algorithm on the encrypted database. Our algorithm preserves both data privacy and query privacy. It also hides data access patterns while supporting efficient query processing. To achieve this, we devise an encrypted index search scheme which can perform data filtering without revealing data access patterns. Through the performance analysis, we verify that our proposed algorithm shows better performance than the existing algorithms in terms of query processing times.

• The Journal of Society for e-Business Studies
• /
• v.11 no.1
• /
• pp.1-23
• /
• 2006

As data on the Web is more and more in XML due to proliferation of Web applications such as e-Commerce, it is strongly required to rapidly process XML queries. One of such techniques is XML query caching. For frequently submitted queries, their results could be cached in order to guarantee fast response for the same queries. In this paper, we propose techniques for XML query performance improvement whereby the set of node identifiers(NIS) for an XML query is cached. NIS is most commonly employed as a format of XML query result,, consisting of the identifiers of the XML elements that comprise the query result. With NIS, it is suitable to meet the Web applications data retrieval requirements because reconstruction and/or modification of query results and integration of multiple query results can be efficiently done. Incremental refresh of NIS against its source updates can also be efficiently done. When the query result is requested in XML, however, materialization of NIS is needed by retrieving the source XML elements through their identifiers. In this paper, we consider three different types of NISs. proposing the algorithms of their creation, materialization, and incremental refresh. All of them were implemented using an RDBMS. Through a detailed set of performance experiments, we showed the efficiency of the proposed XML query caching techniques.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.9
• /
• pp.243-250
• /
• 2021

Recently, with the development of cloud computing, interest in database outsourcing is increasing. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose a parallel range query processing algorithm that supports privacy protection. The proposed algorithm uses the Paillier encryption system to support data protection, query protection, and access pattern protection. To reduce the operation cost of a checking protocol (SRO) for overlapping regions in the existing algorithm, the efficiency of the SRO protocol is improved through a garbled circuit. The proposed parallel range query processing algorithm is largely composed of two steps. It consists of a parallel kd-tree search step that searches the kd-tree in parallel and safely extracts the data of the leaf node including the query, and a parallel data search step through multiple threads for retrieving the data included in the query area. On the other hand, the proposed algorithm provides high query processing performance through parallelization of secure protocols and index search. We show that the performance of the proposed parallel range query processing algorithm increases in proportion to the number of threads and the proposed algorithm shows performance improvement by about 5 times compared with the existing algorithm.

• Journal of KIISE:Databases
• /
• v.31 no.1
• /
• pp.61-71
• /
• 2004

XML can describe a wide range of data, from regular to irregular and from flat to deeply nested. Thus, XML is rapidly emerging as the do facto standard for the Web document format since XML supports an efficient data exchange and integration. Also, to retrieve the data represented by XML, several XML query languages are proposed. XML query languages such as XPath and XQuery use path expressions to traverse irregularly structured data which comprise B% elements. To evaluate path expressions, various path indexes are proposed. However, traditional path indexes are constructed by utilizing only the XML data structure. Therefore, in this paper, we propose an adaptive path index which utilizes the XML data structure as well as query workloads. To improve the query performance, the adaptive path index proposed by this paper manages the frequently used paths and the structural summary of the XML data using a hash tree and a graph structure. Experimental results show that the adaptive path index improves the query performance typically 2 to 69 times compared with the existing indexes.